Unit Propagation in a Tableau Framework

2005 ◽  
pp. 338-342
Author(s):  
Gernot Stenz
Keyword(s):  
Unit Propagation

scholarly journals On Reachability, Relevance, and Resolution in the Planning as Satisfiability Approach

2001 ◽  
Vol 14 ◽  
pp. 1-28 ◽  
Author(s):  
R. I. Brafman
Keyword(s):  
Relative Merit ◽  
Pruning Algorithms ◽  
Unit Propagation ◽  
Planning Problems ◽  
Pruning Techniques ◽  
Shed Light ◽  
Pruning Methods

In recent years, there is a growing awareness of the importance of reachability and relevance-based pruning techniques for planning, but little work specifically targets these techniques. In this paper, we compare the ability of two classes of algorithms to propagate and discover reachability and relevance constraints in classical planning problems. The first class of algorithms operates on SAT encoded planning problems obtained using the linear and Graphplan encoding schemes. It applies unit-propagation and more general resolution steps (involving larger clauses) to these plan encodings. The second class operates at the plan level and contains two families of pruning algorithms: Reachable-k and Relevant-k. Reachable-k provides a coherent description of a number of existing forward pruning techniques used in numerous algorithms, while Relevant-k captures different grades of backward pruning. Our results shed light on the ability of different plan-encoding schemes to propagate information forward and backward and on the relative merit of plan-level and SAT-level pruning methods.

Exploiting the real power of unit propagation lookahead

2001 ◽  
Vol 9 ◽  
pp. 59-80 ◽  
Author(s):  
Daniel Le Berre
Keyword(s):  
Real Power ◽  
The Real ◽  
Unit Propagation

Solving satisfiability problems on FPGAs using experimental unit propagation heuristic

1999 ◽  
pp. 709-711
Author(s):  
Takayuki Suyama ◽  
Makoto Yokoo ◽  
Akira Nagoya
Keyword(s):  
Experimental Unit ◽  
Satisfiability Problems ◽  
Unit Propagation

Beyond Unit Propagation in SAT Solving

2011 ◽  
pp. 267-279 ◽  
Author(s):  
Michael Kaufmann ◽  
Stephan Kottler
Keyword(s):  
Sat Solving ◽  
Unit Propagation

scholarly journals Non-clausal Redundancy Properties

2021 ◽  
pp. 252-272
Author(s):  
Lee A. Barnett ◽  
Armin Biere
Keyword(s):  
State Of The Art ◽  
Binary Decision Diagrams ◽  
Gaussian Elimination ◽  
Decision Diagrams ◽  
Binary Decision ◽  
Refutation Systems ◽  
Unit Propagation

AbstractState-of-the-art refutation systems for SAT are largely based on the derivation of clauses meeting some redundancy criteria, ensuring their addition to a formula does not alter its satisfiability. However, there are strong propositional reasoning techniques whose inferences are not easily expressed in such systems. This paper extends the redundancy framework beyond clauses to characterize redundancy for Boolean constraints in general. We show this characterization can be instantiated to develop efficiently checkable refutation systems using redundancy properties for Binary Decision Diagrams (BDDs). Using a form of reverse unit propagation over conjunctions of BDDs, these systems capture, for instance, Gaussian elimination reasoning over XOR constraints encoded in a formula, without the need for clausal translations or extension variables. Notably, these systems generalize those based on the strong Propagation Redundancy (PR) property, without an increase in complexity.

Solving #QBF Using Extension Rule

2010 ◽  
Vol 26-28 ◽  
pp. 250-254
Author(s):  
Jun Ping Zhou ◽  
Chun Guang Zhou ◽  
Yan Dong Zhai ◽  
Yong Juan Yang
Keyword(s):  
Component Analysis ◽  
New Method ◽  
Quantified Boolean Formulas ◽  
Extension Rule ◽  
Boolean Formulas ◽  
Unit Propagation

Extension rule is a new method for computing the number of models for SAT formulae. In this paper, we investigate the use of the extension rule in solving #QBF, i.e., computing the number of Q1x1…Qn xn which makes the Quantified Boolean Formulas (QBF) Q1x1…Qn xnF evaluate to true. We present a #QBF algorithm based on the extension rule, namely QBFMC, which also integrates the unit propagation and the component analysis together. These excellent technologies improve the efficiency of solving #QBF problems efficiently.

scholarly journals Inconsistency Proofs for ASP: The ASP - DRUPE Format

2019 ◽  
Vol 19 (5-6) ◽  
pp. 891-907
Author(s):  
MARIO ALVIANO ◽  
CARMINE DODARO ◽  
JOHANNES K. FICHTE ◽  
MARKUS HECHER ◽  
TOBIAS PHILIPP ◽  
...  
Keyword(s):  
Logic Program ◽  
Answer Set Programming ◽  
Boolean Satisfiability ◽  
Logic Programs ◽  
Disjunctive Logic Programs ◽  
Normal Logic ◽  
Consistency Problem ◽  
Unit Propagation ◽  
Answer Sets ◽  
Answer Set

AbstractAnswer Set Programming (ASP) solvers are highly-tuned and complex procedures that implicitly solve the consistency problem, i.e., deciding whether a logic program admits an answer set. Verifying whether a claimed answer set is formally a correct answer set of the program can be decided in polynomial time for (normal) programs. However, it is far from immediate to verify whether a program that is claimed to be inconsistent, indeed does not admit any answer sets. In this paper, we address this problem and develop the new proof format ASP-DRUPE for propositional, disjunctive logic programs, including weight and choice rules. ASP-DRUPE is based on the Reverse Unit Propagation (RUP) format designed for Boolean satisfiability. We establish correctness of ASP-DRUPE and discuss how to integrate it into modern ASP solvers. Later, we provide an implementation of ASP-DRUPE into the wasp solver for normal logic programs.

scholarly journals Optimal Implementation of Watched Literals and More General Techniques

2013 ◽  
Vol 48 ◽  
pp. 231-252 ◽  
Author(s):  
I. P. Gent
Keyword(s):  
Constraint Satisfaction ◽  
General Framework ◽  
Search Tree ◽  
Constant Factor ◽  
Worst Case ◽  
Backtracking Search ◽  
Run Time ◽  
Unit Propagation ◽  
Implementation Technique ◽  
Optimal Implementation

I prove that an implementation technique for scanning lists in backtracking search algorithms is optimal. The result applies to a simple general framework, which I present: applications include watched literal unit propagation in SAT and a number of examples in constraint satisfaction. Techniques like watched literals are known to be highly space efficient and effective in practice. When implemented in the `circular' approach described here, these techniques also have optimal run time per branch in big-O terms when amortized across a search tree. This also applies when multiple list elements must be found. The constant factor overhead of the worst case is only 2. Replacing the existing non-optimal implementation of unit propagation in MiniSat speeds up propagation by 29%, though this is not enough to improve overall run time significantly.

Sign in / Sign up

Export Citation Format

Share Document